Multilingual subjectivity analysis using machine translation

Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, we attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), we show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.

Automatic text summarization with Maximal Frequent Sequences

En las últimas dos décadas un aumento exponencial de la información electrónica ha provocado una gran necesidad de entender rápidamente grandes volúmenes de información. En este libro se desarrollan los métodos automáticos para producir un resumen. Un resumen es un texto corto que transmite la información más importante de un documento o de una colección de documentos. Los resúmenes utilizados en este libro son extractivos: una selección de las oraciones más importantes del texto. Otros retos consisten en generar resúmenes de manera independiente de lenguaje y dominio. Se describe la identificación de cuatro etapas para generación de resúmenes extractivos. La primera etapa es la selección de términos, en la que uno tiene que decidir qué unidades contarían como términos individuales. El proceso de estimación de la utilidad de los términos individuales se llama etapa de pesado de términos. El siguiente paso se denota como pesado de oraciones, donde todas las secuencias reciben alguna medida numérica de acuerdo con la utilidad de términos. Finalmente, el proceso de selección de las oraciones más importantes se llama selección de oraciones. Los diferentes métodos para generación de resúmenes extractivos pueden ser caracterizados como representan estas etapas. En este libro se describe la etapa de selección de términos, en la que la detección de descripciones multipalabra se realiza considerando Secuencias Frecuentes Maximales (sfms), las cuales adquieren un significado importante, mientras Secuencias Frecuentes (sf) no maximales, que son partes de otros sf, no deben de ser consideradas. En la motivación se consideró costo vs. beneficio: existen muchas sf no maximales, mientras que la probabilidad de adquirir un significado importante es baja. De todos modos, las sfms representan todas las sfs en el modo compacto: todas las sfs podrían ser obtenidas a partir de todas las sfms explotando cada sfm al conjunto de todas sus subsecuencias. Se presentan los nuevos métodos basados en grafos, algoritmos de agrupamiento y algoritmos genéticos, los cuales facilitan la tarea de generación de resúmenes de textos. Se ha experimentado diferentes combinaciones de las opciones de selección de términos, pesado de términos, pesado de oraciones y selección de oraciones para generar los resúmenes extractivos de textos independientes de lenguaje y dominio para una colección de noticias. Se ha analizado algunas opciones basadas en descripciones multipalabra considerándolas en los métodos de grafos, algoritmos de agrupamiento y algoritmos genéticos. Se han obtenido los resultados superiores al de estado de arte. Este libro está dirigido a los estudiantes y científicos del área de Lingüística Computacional, y también a quienes quieren saber sobre los recientes avances en las investigaciones de generación automática de resúmenes de textos.In the last two decades, an exponential increase in the available electronic information causes a big necessity to quickly understand large volumes of information. It raises the importance of the development of automatic methods for detecting the most relevant content of a document in order to produce a shorter text. Automatic Text Summarization (ats) is an active research area dedicated to generate abstractive and extractive summaries not only for a single document, but also for a collection of documents. Other necessity consists in finding method for ats in a language and domain independent way. In this book we consider extractive text summarization for single document task. We have identified that a typical extractive summarization method consists in four steps. First step is a term selection where one should decide what units will count as individual terms. The process of estimating the usefulness of the individual terms is called term weighting step. The next step denotes as sentence weighting where all the sentences receive some numerical measure according to the usefulness of its terms. Finally, the process of selecting the most relevant sentences calls sentence selection. Different extractive summarization methods can be characterized how they perform these steps. In this book, in the term selection step, we describe how to detect multiword descriptions considering Maximal Frequent Sequences (mfss), which bearing important meaning, while non-maximal frequent sequences (fss), those that are parts of another fs, should not be considered. Our additional motivation was cost vs. benefit considerations: there are too many non-maximal fss while their probability to bear important meaning is lower. In any case, mfss represent all fss in a compact way: all fss can be obtained from all mfss by bursting each mfs into a set of all its subsequences.New methods based on graph algorithms, genetic algorithms, and clustering algorithms which facilitate the text summarization task are presented. We have tested different combinations of term selection, term weighting, sentence weighting and sentence selection options for language-and domain-independent extractive single-document text summarization on a news report collection. We analyzed several options based on mfss, considering them with graph, genetic, and clustering algorithms. We obtained results superior to the existing state-ofthe- art methods. This book is addressed for students and scientists of the area of Computational Linguistics, and also who wants to know recent developments in the area of Automatic Text Generation of Summaries

Effectiveness gain of polarity detection through topic domains

National audienceMost of the work on polarity detection consists in finding out negative or positive words in a document using sentiment lexical resources. Indeed, some versions of such approaches have performed well but most of these approaches rely only on prior polarity of words and do not exploit the contextual polarity of words. Sentiment semantics of a term vary from one domain to another. For example, the word "unpredictable" conveys a positive feeling about a movie plot, but the same word conveys negative feeling in context of operating of a digital camera. In this work, we demonstrate this aspect of sentiment polarity. We use TREC Blog 2006 Data collection with topics of TREC Blog 2006 and 2007 for experimentation. The results of our experiments showed an improvement (95%) on polarity detection. The conclusion is that the context plays a role on the polarity of each word

Unsupervised Sense-Aware Hypernymy Extraction

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austri

Improving Hypernymy Extraction with Distributional Semantic Classes

In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms. We present methods for inducing sense-aware semantic classes using distributional semantics and using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On the one hand, this allows us to filter out wrong extractions using the global structure of distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction in terms of both precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a SemEval'16 task on taxonomy induction.Comment: In Proceedings of the 11th Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japa